Reappraising the utility of Google Flu Trends

PLoS Computational Biology
Sasikiran Kandula, Jeffrey Shaman

Abstract

Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources-GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting-five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were ...Continue Reading

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Citations

Apr 17, 2020·JMIR Public Health and Surveillance·Sultan MahmoodAlain Labrique
May 14, 2020·JMIR Public Health and Surveillance·Thomas S HigginsJonathan Y Ting
Oct 17, 2020·SN Computer Science·Enoch Arulprakash, Martin Aruldoss
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May 22, 2021·Nature Communications·Dave Osthus, Kelly R Moran
May 18, 2021·Mayo Clinic Proceedings. Innovations, Quality & Outcomes·Omar DzayeMichael J Blaha

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Software Mentioned

FluSurv
GFT
epidata API
Google search engine
ILIf
Google Flu Trends
FluView
Google Trends API
NET
Health Trends API

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